Use of Bayesian Network characteristics to link project management
maturity and risk of project overcost
- URL: http://arxiv.org/abs/2009.09828v1
- Date: Fri, 18 Sep 2020 08:00:25 GMT
- Title: Use of Bayesian Network characteristics to link project management
maturity and risk of project overcost
- Abstract summary: This paper uses the characteristics of Bayesian networks to formalize experts' knowledge and to extract knowledge from a project overcost database.
It develops a method to estimate the impact of project management maturity on the risk of project overcost.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The project management field has the imperative to increase the project
probability of success. Experts have developed several project management
maturity models to assets and improve the project outcome. However, the current
literature lacks of models allowing correlating the measured maturity and the
expected probability of success. This paper uses the characteristics of
Bayesian networks to formalize experts' knowledge and to extract knowledge from
a project overcost database. It develops a method to estimate the impact of
project management maturity on the risk of project overcost. A general
framework is presented. An industrial case is used to illustrate the
application of the method.
Related papers
- Ontology-supported Design Parameter Management for Change Impact Analysis [65.57339762906196]
OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems.<n>The approach is suitable for both software and hardware designs.
arXiv Detail & Related papers (2026-08-22T13:25:31Z) - Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents [69.57766200248706]
We study marginal value estimation for context management in deep research agents.<n>We evaluate lightweight criteria and a learned value model at pre-retrieval, post-retrieval, and pre-synthesis stages.<n> Lightweights reduce token usage by up to 73% with little quality degradation, learned pruning remains competitive on selected trade-offs, and no single method dominates across quality, efficiency, and faithfulness.
arXiv Detail & Related papers (2026-08-09T00:56:37Z) - Predicting Agile Success: The Critical Few Factors [0.0]
The study developed a novel model that can be used to evaluate and measure project success.<n>This research found that a few critical factors significantly contribute to Agile project success.
arXiv Detail & Related papers (2026-08-06T16:18:13Z) - Project resilience as network robustness [0.552480439325792]
Some project members are specialists undertaking only few tasks, whereas other are generalists and are responsible for the success of many tasks.<n>The reliance of a project on a few key people can lead to severe economic losses and delays.<n>We develop a novel method to assess project vulnerability by looking at it from the lens of network robustness.
arXiv Detail & Related papers (2026-04-19T12:55:13Z) - Multi-Ecosystem Modeling of OSS Project Sustainability [4.305183426378826]
We present an empirical study and quantitative analysis of the sustainability of incubator projects in the Apache, Eclipse, and OSGeo foundations.<n>We develop foundation-specific sustainability models and a project triage, based on projects' sociotechnical trace profiles.
arXiv Detail & Related papers (2026-02-19T06:17:54Z) - When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective [45.419765404078724]
We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge.<n>We propose schemes to operationalize these theoretical metrics, and thereby develop a method that can predict the effectiveness of pretext tasks in advance.
arXiv Detail & Related papers (2025-08-10T11:23:36Z) - Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Model [0.0]
How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty.
We propose comparison rules based on Quicksort and the Bradley--Terry model, which connects rankings to pairwise "win" probabilities.
arXiv Detail & Related papers (2025-04-06T23:16:30Z) - CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing [56.98081258047281]
Collaborative Inference with Token-lEvel Routing (CITER) is a framework that enables efficient collaboration between small and large language models.
We formulate router training as a policy optimization, where the router receives rewards based on both the quality of predictions and the inference costs of generation.
Our experiments show that CITER reduces the inference costs while preserving high-quality generation, offering a promising solution for real-time and resource-constrained applications.
arXiv Detail & Related papers (2025-02-04T03:36:44Z) - Design of mechanisms for ensuring the execution of tasks in project planning [0.0]
The paper reports an analysis of aspects of the project planning stage.
It takes into account restrictions on financial costs and duration of project implementation.
Models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed.
arXiv Detail & Related papers (2025-01-02T13:47:20Z) - Enhancing Project Performance Forecasting using Machine Learning Techniques [0.0]
This research proposes a machine learning-based approach to forecast project performance metrics.
It incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts.
The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project.
arXiv Detail & Related papers (2024-11-26T22:09:55Z) - FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents [64.1759086221016]
We present FlowBench, the first benchmark for workflow-guided planning.
FlowBench covers 51 different scenarios from 6 domains, with knowledge presented in diverse formats.
Results indicate that current LLM agents need considerable improvements for satisfactory planning.
arXiv Detail & Related papers (2024-06-21T06:13:00Z) - An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models [55.01592097059969]
Supervised finetuning on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities.
Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool.
We propose using experimental design to circumvent the computational bottlenecks of active learning.
arXiv Detail & Related papers (2024-01-12T16:56:54Z) - DevEval: Evaluating Code Generation in Practical Software Projects [52.16841274646796]
We propose a new benchmark named DevEval, aligned with Developers' experiences in practical projects.
DevEval is collected through a rigorous pipeline, containing 2,690 samples from 119 practical projects.
We assess five popular LLMs on DevEval and reveal their actual abilities in code generation.
arXiv Detail & Related papers (2024-01-12T06:51:30Z) - Performance-lossless Black-box Model Watermarking [69.22653003059031]
We propose a branch backdoor-based model watermarking protocol to protect model intellectual property.
In addition, we analyze the potential threats to the protocol and provide a secure and feasible watermarking instance for language models.
arXiv Detail & Related papers (2023-12-11T16:14:04Z) - Data-Driven Risk Modeling for Infrastructure Projects Using Artificial
Intelligence Techniques [0.0]
This study introduces a data-driven framework to identify risks automatically and to examine the quality of early risk registers and risk assessments.
Risk registers from more than 70 U.S. major transportation projects form the input dataset.
arXiv Detail & Related papers (2023-11-23T21:02:54Z) - Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling [0.43512163406552007]
A planner in charge of project planning has to select a set of activities to perform, determine their precedence constraints, and schedule them according to temporal project constraints.
We suggest a data-driven project planning approach for classes of projects such as infrastructure building and information systems development projects.
arXiv Detail & Related papers (2023-11-20T05:13:17Z) - Individual context-free online community health indicators fail to identify open source software sustainability [3.192308005611312]
We monitored thirty-eight open source projects over the period of a year.
None of the projects were abandoned during this period, and only one project entered a planned shutdown.
Results were highly heterogeneous, showing little commonality across documentation, mean response times for issues and code contributions, and available funding/staffing resources.
arXiv Detail & Related papers (2023-09-21T14:41:41Z) - How Early Participation Determines Long-Term Sustained Activity in
GitHub Projects? [20.236570418427533]
We aim to explore the relationship between early participation factors and long-term project sustainability.
We leverage a novel methodology combining the Blumberg model of performance and machine learning to predict the sustainability of 290,255 GitHub projects.
We quantitatively show that early participants have a positive effect on project's future sustained activity if they have prior experience in OSS project incubation.
arXiv Detail & Related papers (2023-08-11T08:24:41Z) - A Proposed Framework for the Comprehensive Scalability Assessment of
ICTD Projects [0.0]
Scalability of ICTD projects is an imperative topic that has been neglected in the field.
This study proposes a Comprehensive Scalability Assessment Framework (CSAF), using systems theory and amplification theory.
arXiv Detail & Related papers (2021-08-22T15:29:53Z) - Estimating Fund-Raising Performance for Start-up Projects from a Market
Graph Perspective [58.353799280109904]
We propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
Specifically, we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
arXiv Detail & Related papers (2021-05-27T02:39:30Z) - Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
We propose a cost-sensitive portfolio selection method with deep reinforcement learning.
Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations.
A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
arXiv Detail & Related papers (2020-03-06T06:28:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.